Recent work in qualitative reasoning has focused on predicting the dynamic behavior of continuous physical systems. Significant headway has been made in identifying the principles necessary to predict this class of behavior. However, the predictive inference engines based on these principles are limited in their ability to reason about time. This paper presents a general approach to behavioral prediction which overcomes many of these limitations. Generality results from a clean separation between principles relating to time, continuity, and qualitative representations. The resulting inference mechanism, based on propagation of constraints, is applicable to a wide class of physical systems exhibiting discrete or continuous behavior, and can be used with a variety of representations (e.g., digital, quantitative, qualitative or symbolic abstractions). In addition, it provides a framework in which to explore a broad range of tasks including prediction, explanation, diagnosis, and design.

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